This study presents a comprehensive scientometric analysis of research productivity on Coronary Artery Disease (CAD) among the BRICS countries, Brazil, Russia, India, China, and South Africa, using data retrieved from the Web of Science database for the period 1990 to 2019. A total of 50,036 records were analyzed to assess publication growth trends, authorship patterns, collaboration levels, and citation impact. The findings reveal a steady increase in CAD-related publications, with China emerging as the leading contributor, followed by Brazil, Russia, India, and South Africa. English dominated as the primary language of communication, accounting for over 93% of publications. Authorship and collaboration analysis indicate a high degree of joint research, with 97.91% of studies being co-authored and a degree of collaboration of 0.98, underscoring the collective nature of scientific inquiry in this domain. The study validates the applicability of Lotkas Law for author productivity, Bradfords Law for journal distribution, and Zipfs Law for keyword frequency, while the Price Square Root Law was found inapplicable. The predominant publication format was journal articles (79.7%), and Kardiologiya (Russia) emerged as the most prolific journal. The results demonstrate significant growth in CAD research output and collaboration within BRICS, though notable disparities persist among member nations. The study recommends enhancing individual author productivity, expanding international collaboration, and supporting CAD research through strategic institutional and governmental initiatives. These findings provide valuable insights for policymakers, funding agencies, and the academic community to strengthen cardiovascular research capacity within developing economies.
Andreas Kamilaris, Chirag Padubidri, Asfa Jamil
et al.
This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, as well as insurance companies and banks that have properties in their portfolio. This paper demonstrates the power, potential, current and future challenges of geospatial analytics and environmental digital twins on a large scale.
We present sum-of-squares spectral amplification (SOSSA), a framework for improving quantum simulation relevant to low-energy problems. We show how SOSSA can be applied to problems like energy and phase estimation and provide fast quantum algorithms for these problems that significantly improve over prior art. To illustrate the power of SOSSA in applications, we consider the Sachdev-Ye-Kitaev model, a representative strongly correlated system, and demonstrate asymptotic speedups over generic simulation methods by a factor of the square root of the system size. Our results reinforce those observed in [G.H. Low \textit{et al.}, arXiv:2502.15882 (2025)], where SOSSA was used to achieve state-of-the-art gate costs for phase estimation of real-world quantum chemistry systems.
Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. As a result, uncertainty prediction models for a country are becoming increasingly important in the real world, and they are expanding to provide more input from 'big data' collections, as well as the interconnectedness of global economies and social networks. This has culminated in massive volumes of qualitative data from outlets like television, print, digital, and social media, necessitating the use of artificial intelligence (AI) tools like machine learning to make sense of it all and promote predictive precision [1]. The Global Database of Activities, Voice, and Tone (GDELT Project) records broadcast, print, and web news in over 100 languages every second of every day, identifying the people, locations, organisations, counts, themes, outlets, and events that propel our global community and offering a free open platform for computation on the entire world. The main goal of our research is to investigate how, when our data grows more voluminous and fine-grained, we can conduct a more complex methodological analysis of political conflict. The GDELT dataset, which was released in 2012, is the first and potentially the most technologically sophisticated publicly accessible dataset on political conflict.
This study examines the transformative potential of Generative AI (GenAI) in teacher education within developing countries, focusing on Ghana, where challenges such as limited pedagogical modeling, performance-based assessments, and practitioner-expertise gaps hinder progress. GenAI has the capacity to address these issues by supporting content knowledge acquisition, a role that currently dominates teacher education programs. By taking on this foundational role, GenAI allows teacher educators to redirect their focus to other critical areas, including pedagogical modeling, authentic assessments, and fostering digital literacy and critical thinking. These roles are interconnected, creating a ripple effect where pre-service teachers (PSTs) are better equipped to enhance K-12 learning outcomes and align education with workforce needs. The study emphasizes that GenAI's roles are multifaceted, directly addressing resistance to change, improving resource accessibility, and supporting teacher professional development. However, it cautions against misuse, which could undermine critical thinking and creativity, essential skills nurtured through traditional teaching methods. To ensure responsible and effective integration, the study advocates a scaffolding approach to GenAI literacy. This includes educating PSTs on its supportive role, training them in ethical use and prompt engineering, and equipping them to critically assess AI-generated content for biases and validity. The study concludes by recommending empirical research to explore these roles further and develop practical steps for integrating GenAI into teacher education systems responsibly and effectively.
Cross country running races are different to track and road races in that the courses are not typically accurately measured and the condition of the course can have a strong effect on the finish times of the participants. In this paper we investigate these effects by modelling the finish times of all participants in 28 cross country running races over 5 seasons in the North East of England. We model the natural logarithm of the finish times using linear mixed effects models for both the senior men's and senior women's races. We investigate the effects of weather and underfoot conditions using windspeed and rainfall as covariates, fit distance as a covariate, and investigate the effect of time via the season of the race, in particular investigating any evidence of a pre- to post-Covid effect. We use random athlete effects to model the participant to participant variability and identify the most difficult courses using random course effects. The statistical inference is Bayesian. We assess model adequacy by comparing samples from the posterior predictive distribution of finish times to the observed distribution of finish times in each race. We find strong differences between the difficulty of the courses, effects of rainfall in the month of the race and the previous month to increase finish times and an effect of increasing distance increasing finish times. We find no evidence that windspeed affects finish times.
Olga Viberg, Mutlu Cukurova, Yael Feldman-Maggor
et al.
With growing expectations to use AI-based educational technology (AI-EdTech) to improve students' learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers' willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect it to benefit them versus how many concerns it raises for them. In this study, we surveyed 508 K-12 teachers across six countries on four continents to understand which teacher characteristics shape teachers' trust in AI-EdTech, and its proposed antecedents, perceived benefits and concerns about AI-EdTech. We examined a comprehensive set of characteristics including demographic and professional characteristics (age, gender, subject, years of experience, etc.), cultural values (Hofstede's cultural dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), and psychological factors (self-efficacy and understanding). Using multiple regression analysis, we found that teachers with higher AI-EdTech self-efficacy and AI understanding perceive more benefits, fewer concerns, and report more trust in AI-EdTech. We also found geographic and cultural differences in teachers' trust in AI-EdTech, but no demographic differences emerged based on their age, gender, or level of education. The findings provide a comprehensive, international account of factors associated with teachers' trust in AI-EdTech. Efforts to raise teachers' understanding of, and trust in AI-EdTech, while considering their cultural values are encouraged to support its adoption in K-12 education.
Pierre Marchand, Ugo Lebreuilly, Mordecai-Mark Mac Low
et al.
Dust grains influence many aspects of star formation, including planet formation, opacities for radiative transfer, chemistry, and the magnetic field via Ohmic, Hall, and ambipolar diffusion. The size distribution of the dust grains is the primary characteristic influencing all these aspects. Grain size increases by coagulation throughout the star formation process. We describe here numerical simulations of protostellar collapse using methods described in earlier papers of this series. We compute the evolution of the grain size distribution from coagulation and the non-ideal magnetohydrodynamics effects self-consistently and at low numerical cost. We find that the coagulation efficiency is mostly affected by the time spent in high-density regions. Starting from sub-micron radii, grain sizes reach more than 100 μm in an inner protoplanetary disk that is only 1000 years old. We also show that the growth of grains significantly affects the resistivities, and indirectly the dynamics and angular momentum of the disk.
The Aryabhatta Research Institute of Observational Sciences (ARIES), a premier autonomous research institute under the Department of Science and Technology, Government of India has a legacy of about seven decades with contributions made in the field of observational sciences namely atmospheric and astrophysics. The Survey of India used a location at ARIES, determined with an accuracy of better than 10 meters on a world datum through institute participation in a global network of Earth artificial satellites imaging during late 1950. Taking advantage of its high-altitude location, ARIES, for the first time, provided valuable input for climate change studies by long term characterization of physical and chemical properties of aerosols and trace gases in the central Himalayan regions. In astrophysical sciences, the institute has contributed precise and sometime unique observations of the celestial bodies leading to a number of discoveries. With the installation of the 3.6 meter Devasthal optical telescope in the year 2015, India became the only Asian country to join those few nations of the world who are hosting 4 meter class optical telescopes. This telescope, having advantage of geographical location, is well-suited for multi-wavelength observations and for sub-arc-second resolution imaging of the celestial objects including follow-up of the GMRT, AstroSat and gravitational-wave sources.
This text summarizes some of the findings regarding municipal citizenship (vecindad) and membership in the kingdom community (naturaleza) in Spain from the middle ages to the twentieth century and compares and contrasts them with the conclusions reached by Maarten Prak. Basically agreeing with his main discoveries, it nonetheless argues that in Spain, at least, many of the features described by Prak did not disappear in the aftermaths of the French Revolution but instead persisted into the nineteenth and the twentieth century. Furthermore, this text also advocates for the importance of law. It suggests that bringing in the law would have facilitated both the description and the understanding of many of the very interesting phenomenon described by the author.
Florian Huber, Michael Pfarrhofer, Philipp Piribauer
This paper develops a dynamic factor model that uses euro area (EA) country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country-specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent states. We moreover introduce flexible stochastic volatility specifications to control for heteroscedasticity in the measurement errors and innovations to the latent states. Carefully specified shrinkage priors allow for pushing the model towards a homoscedastic specification, if supported by the data. Our measure of the output gap closely tracks other commonly adopted measures, with small differences in magnitudes and timing. To assess whether the model-based output gap helps in forecasting inflation, we perform an out-of-sample forecasting exercise. The findings indicate that our approach yields superior inflation forecasts, both in terms of point and density predictions.
My book Citizens without Nations might be read as a triple provocation, as it challenges three received wisdoms of historical textbooks. The first, and perhaps most fundamental, is the idea that the grand narratives of the past have to be the histories of nations and their states.
While automatic computational techniques appear to reveal novel insights in digital art history, a complementary approach seems to get less attention: that of human annotation. We argue and exemplify that a 'human in the loop' can reveal insights that may be difficult to detect automatically. Specifically, we focussed on perceptual aspects within pictorial art. Using rather simple annotation tasks (e.g. delineate human lengths, indicate highlights and classify gaze direction) we could both replicate earlier findings and reveal novel insights into pictorial conventions. We found that Canaletto depicted human figures in rather accurate perspective, varied viewpoint elevation between approximately 3 and 9 meters and highly preferred light directions parallel to the projection plane. Furthermore, we found that taking the averaged images of leftward looking faces reveals a woman, and for rightward looking faces showed a male, confirming earlier accounts on lateral gender bias in pictorial art. Lastly, we confirmed and refined the well-known light-from-the-left bias. Together, the annotations, analyses and results exemplify how human annotation can contribute and complement to technical and digital art history.
In models of galaxy formation, feedback driven both by supernova (SN) and active galactic nucleus (AGN) is not efficient enough to quench star formation in massive galaxies. Models of smaller galaxies have suggested that cosmic rays (CRs) play a major role in expelling material from the star forming regions by diffusing SN energy to the lower density outskirts. We therefore run gas dynamical simulations of galactic outflows from a galaxy contained in a halo with $5\times10^{12}$~M$_{\odot}$ that resembles a local ultraluminous galaxy, including both SN thermal energy and a treatment of CRs using the same diffusion approximation as Salem & Bryan. We find that CR pressure drives a low-density bubble beyond the edge of the shell swept up by thermal pressure, but the main bubble driven by SN thermal pressure overtakes it later, which creates a large-scale biconical outflow. CRs diffusing into the disk are unable to entrain its gas in the outflows, yielding a mass-loading rate of only $\sim0.1~\%$ with varied CR diffusion coefficients. We find no significant difference in mass-loading rates in SN driven outflows with or without CR pressure. Our simulations strongly suggest that it is hard to drive a heavily mass-loaded outflow with CRs from a massive halo potential, although more distributed star formation could lead to a different result.
There are known gender imbalances in participation in scientific fields, from female dominance of nursing to male dominance of mathematics. It is not clear whether there is also a citation imbalance, with some claiming that male-authored research tends to be more cited. No previous study has assessed gender differences in the readers of academic research on a large scale, however. In response, this article assesses whether there are gender differences in the average citations and/or Mendeley readers of academic publications. Field normalised logged Scopus citations and Mendeley readers from mid-2018 for articles published in 2014 were investigated for articles with first authors from India, Spain, Turkey, the UK and the USA in up to 251 fields with at least 50 male and female authors. Although female-authored research is less cited in Turkey (-4.0%) and India (-3.6%), it is marginally more cited in Spain (0.4%), the UK (0.4%), and the USA (0.2%). Female-authored research has fewer Mendeley readers in India (-1.1%) but more in Spain (1.4%), Turkey (1.1%), the UK (2.7%) and the USA (3.0%). Thus, whilst there may be little practical gender difference in citation impact in countries with mature science systems, the higher female readership impact suggests a wider audience for female-authored research. The results show that the conclusions from a gender analysis depend on the field normalisation method. A theoretically informed decision must therefore be made about which normalisation to use. The results also suggest that arithmetic mean-based field normalisation is favourable to males.